What is Population Health?

Long-term care for the aging is a growing need in the United States. Each day 10,000 people turn 65 in the US and continues to trend upward. This is often referred to as the “Silver Tsunami”.  As people age they suffer from more medical challenges. In fact, it is estimated that the cost of healthcare for the aging represent 1/3 of all US healthcare (over $1 Trillion each year). Without getting all “statistically” on you, once someone reaches the age of 65 their life expectancy approaches an average of +20 years (higher for women and lower for men).  It is this 20-year span where we see long-term care increase dramatically.

To address this reality, states have implemented long-term care assessment forms for the purpose of identifying needs and tracking individual patient health. While the assessment forms vary by state, the overall content is very similar.  A range of questions are asked to identify the current health conditions, mental health, living conditions, cognitive abilities, and an assortment of Activities of Daily Living “ADL” (basic self-care tasks such as bating, toileting, grooming dressing, and self-feeding), and Instrumental Activities of Daily Living “IADL” (shopping, housekeeping, accounting, meal preparation, and transportation and communication needs). The assessment is also designed to capture the degree of care one receives when compared to the need for care, as well as any current or needed assistive aids.

While this assessment is quite useful for the individual in order to assess needs and assistance, it is somewhat one-dimensional in that it is used for a single purpose. If, however, the overall population is studied, some interesting insights can be gleaned. Through these insights changes in the care plan protocols can be introduced in a proactive manner to lessen the likelihood of adverse events. For example, if it is observed that there is a statistical relevance to the type of meal plan an individual is on relative to staving off Type II diabetes, this meal plan can become the recommended plan for all pre-diabetics. Similarly, if it is shown that a disproportionate number of the elderly that have a fall also have a large dog, a new protocol might be proposed that implements a dog walker in order to reduce falls caused by a pet.

While each individual’s health history can track specific measures of wellness, the combination of these measures can provide newly synthesized methods of determining correlations, and ultimately indications of causality. Combining living situation and cognitive issues can provide insights, as can the among of care provided and overall feelings of wellness. By combining aspects of living situation, mental health, nutrition, mobility needs, medications, and more, a vast array of correlations can be observed, and likely contributing factors can be discovered.

Larger populations with greater data sources lead to greater statistical relevance. For example, enriching existing data sources to include EHR, claims, appointments, vitals, and family history allows for a more complete picture. Further augmenting this with vitals data obtained from Remote Patient Monitoring (RPM) and Medical IoT’s (Internet of Things devices), the information can not only extend in breadth but in frequency. With this timely and extensive view of the individual and the overall population, a number of additional benefits can be derived including threshold-based alerts, reminders, and even machine learning algorithms that predict the likelihood of adverse events proactively. The population view also allows for unique tools that allow for rapid experiments in the care protocol to validate changes applied to a study group to statistically validate effectiveness prior to a broader adoption. Believe that a change in a meal plan will reduce Type II diabetes? Run a rapid, low cost experiment against a study group to prove the plan works. Think a dog walker will reduce falls? Test the hypothesis to understand the cost/benefit. Since the test and control populations are contained within the population health platform, experiment results can be monitored in near real time.

With the cost of adverse events running in the tens of thousands of dollars, and having long-term adverse effects, it is easy to see why we must shift from episodic care to a proactive model, and how Population Health is a critical component in this change.

Learn More about the Lifemesh Population Health Solutions.